Search

Aims: Lipid peroxidation occurring in lung adenocarcinoma (LUAD) cells leads to ferroptosis. Lysophosphatidylcholine acyl-transferase 3 (LPCAT3) plays a key role in providing raw materials for lipid peroxidation by promoting esterification of polyunsaturated fatty acids to phospholipids. Whether LPCAT3 determines ferroptosis sensitivity and the mechanism by which its expression is regulated in LUAD has not been reported. Results: LPCAT3 and acyl-coenzyme A (CoA) synthetase long-chain family member (ACSL)4 levels were positively associated with ferroptosis sensitivity in LUAD cell lines. Overexpression of LPCAT3 and ACSL4 sensitized LUAD cells to ferroptosis, while LPCAT3 and ACSL4 knockout showed the opposite effect. Zinc-finger E-box-binding (ZEB) was shown to directly bind the LPCAT3 promoter to stimulate its transcription in a Yes-associated protein (YAP)-dependent manner. An interaction between YAP and ZEB was also observed. E1A-binding protein p300 (EP300) simultaneously bound with YAP and ZEB, and induced H3K27Ac for LPCAT3 transcription. This mechanism was verified in primary LUAD cell and xenograft models. The ACSL4, LPCAT3, and YAP combination can jointly determine LUAD ferroptosis sensitivity. Innovation: The binding site of ZEB exists in the −1600 to −1401 nt region of LPCAT3 promoter, which promotes LPCAT3 transcription after ZEB binding. ZEB and YAP bind, and the ZEB zinc-finger cluster domain and YAP WW domain are crucial for their binding. EP300 may bind with YAP via its Bromo domain and with ZEB via its CBP/p300-HAT domain. In addition, the combination of ACSL4, LPCAT3, and YAP to determine ferroptosis sensitivity of LUAD cells is better than prostaglandin-endoperoxide synthase 2 (PTGS2), transferrin receptor (TFRC), or NADPH oxidase 1 (NOX1). Conclusion: LPCAT3 transcription is regulated by YAP, ZEB, and EP300. LUAD ferroptosis sensitivity can be determined by the combination of ACSL4, LPCAT3, and YAP. Antioxid. Redox Signal. 39, 491–511. [ABSTRACT FROM AUTHOR]

The executor is an important component of a database. Typical executors that are applied in modern database systems follow either the VOLCANO model or Compiled model, each of which fits some scenarios but not all. Even the widely employed PostgreSQL (PGSQL) and CockroachDB (CRDB) have to rely on human experts to achieve the optimal execution mode. Nevertheless, the accuracy of these decisions is only 32.8% on average, even with an expert involved. Moreover, due to the exclusive use of rule-based strategies, it is not feasible to reasonably switch between two working modes when confronted with different queries. To solve this problem, we propose a QUery awARe daTabase adaptivE compilaTion decision system (Quartet), which can determine the most suitable execution mode with respect to the current workload at runtime. Quartet generates operator-cost and tree-based vectors by analysing the query execution plan (QEP) and then uses the fully connected neural network (FCNN) and tree-based convolutional neural network (TBCNN) to learn the relationship between the QEP and the optimal execution. Our evaluations show that Quartet can improve execution decision accuracy by 60% on average under TPC-H (under 3 GB) workloads. • Enhance accuracy in choosing traditional or expression compilation iterative models. • Efficiently generates ML feature vectors from query execution plans. • Support for different workloads by using multiple Machine Learning models. [ABSTRACT FROM AUTHOR]

An optical fiber humidity sensor based on the extrinsic Fabry–Perot interference (EFPI) structure has been proposed. Polymethyl methacrylate (PMMA) film was inserted as the humidity sensing actuator between a coreless fiber (CLF) and the single mode fiber (SMF). The relative humidity (RH) sensing performance has been experimentally demonstrated with the sensitivity of 0.1747 nm/%RH and response time of 4.5 min in the range of 25–80%RH. The temperature depend-free property has been verified during 30–55 °C, revealing its good stability in the practical environment. Due to the compact structure, miniature scale, the proposed fiber probe can be integrated with the common SMF system to determine the environmental humidity in the way of the remote and distributed network. [ABSTRACT FROM AUTHOR]

Radial harmonic‐Fourier moments (RHFMs) are popular for image reconstruction and invariant pattern recognition due to their properties of translation, scaling and rotation invariant. RHFMs possess lower computation complexity as compared to Zernike moments and Bessel‐Fourier moments. However, they always suffer from discontinuity, numerical instability near the center of image, and reconstruction error, especially have a rise for higher order of moments. In this paper, an improvement of radial harmonic‐Fourier moments (IRHFMs) is proposed for effectively avoiding the above‐mentioned problems.In this paper, a 2D fast Fourier transform algorithm also is applied to the image matrix to obtain the IRHFMs. Simulation experimental results demonstrate the proposed IRHFMs perform better than traditional RHFMs and other classic orthogonal moments including the latest image moments, for example, polar harmonic Fourier moments in terms of the image reconstruction capability and rotation invariant recognition accuracy in noise‐free and noisy conditions. [ABSTRACT FROM AUTHOR]

In order to improve the performance of image reconstruction and image recognition accuracy in classic image orthogonal moments, a new set of moments based on the weighted spherical Bessel polynomial of the first kind is proposed, named weighted spherical Bessel–Fourier moments (WSBFMs), which are orthogonal in polar coordinate domain and can be thought as generalized and orthogonal complex moments. Then, the set of proposed WSBFMs is derived from the weighted spherical Bessel polynomial and image rotation-invariant is easily to achieve. Compared with Zernike, orthogonal Fourier–Mellion and Bessel polynomials of the same degree, the weighted spherical Bessel orthogonal radial polynomials have more zeros value, and these zeros value are more uniformly distributed. It makes WSBFMs more suitable for geometric invariant recognition as a generalization of orthogonal complex moments. Finally, Theoretical and experimental results show the superiority of the new orthogonal moments in terms of image reconstruction capability and invariant object recognition accuracy under noise-free, noisy and smooth distortion condition. [ABSTRACT FROM AUTHOR]

The aluminophosphate glasses were prepared by the melting-quenching technique and their optical and structural properties were investigated. The 56P 2 O 5 -20Al 2 O 3 glass exhibits unique blue emission at 406 nm due to the phosphorus defect in the glass matrix. Addition of SiO 2 , ZnF 2 , or Li 2 O to 56P 2 O 5 -20Al 2 O 3 glass matrix has different influence on formation of phosphorus defect resulting in different intensities of blue emission at 406 nm, though SiO 2 , ZnF 2 or Li 2 O mostly enters the glass network as modifier. Structural analysis of P 2 O 5 -Al 2 O 3 based aluminophosphate glasses by positron annihilation lifetime spectroscopy indicates that values of positron lifetimes and the corresponding intensities may be connected with the sizes and number of structural defects. Under excitation of 251 nm, a broad blue emission band and a red emission band were observed in the Mn-doped 56P 2 O 5 -20Al 2 O 3 -15SiO 2 -10ZnF 2 glass, indicating the operation of energy transfer from the phosphorus defect to octahedrally coordinated Mn in the glass host. [ABSTRACT FROM AUTHOR]

Nearest neighbor (NN) search in high-dimensional space plays a fundamental role in large-scale image retrieval. It seeks efficient indexing and search techniques, both of which are simultaneously essential for similarity search and semantic analysis. However, in recent years, there has been a rare breakthrough. Achievement of current techniques for NN search is far from satisfactory, especially for exact NN search. A recently proposed method, HB, addresses the exact NN search efficiently in high-dimensional space. It benefits from cluster-based techniques which can generate more compact representation of the data set than other techniques by exploiting interdimensional correlations. However, HB suffers from huge cost for lower bound computations and provides no further pruning scheme for points in candidate clusters. In this paper, we extend the HB method to address exact NN search in correlated, high-dimensional vector data sets extracted from large-scale image database by introducing two new pruning/selection techniques and we call it HB+. The first approach aims at selecting more quickly the subset of hyperplanes/clusters that must be considered. The second technique prunes irrelevant points in the selected subset of clusters. Performed experiments show the improvement of HB+ with respect to HB in terms of efficiency (I/O cost and CPU response time) and also demonstrate the superiority over other exact NN indexes. [ABSTRACT FROM AUTHOR]

The emergence of the online media sharing sites (e.g. Youtube, Youku, and Hulu) have introduced new challenges in program recommendation in online networks. However, there is a bottleneck that the amount of available viewing logs and user friendship networks are too limited to design effective recommendation algorithms. Thus, carrying out an intelligent program recommendation system is important for these sites. In this work, we propose a novel model which turns to the social networks and mine user preferences information expressed in microblogs for evaluating the similarity between online movies and TV episodes. To the best of our knowledge, it is the first effort to bridge the gap between movie and TV watchers domain with social network activities. Moreover, it is the first approach that can solve the “cold-start” problem in movie and TV recommendation system. Series of data mining approaches and social computing models have been adopted in this work. Similar programs found from the social network are further used to suggest programs in other media devices. This work can be easily applied in online media streaming sites in order that intelligent recommendations of programs can be made to the customers through mining microblogs. [ABSTRACT FROM AUTHOR]

Histogram representations of visual features, such as high dimensional Bag-of-Features (BOF) and Spatial Pyramid Matching (SPM) representation, have been widely studied and adopted in image classification and retrieval due to their simplicity and performance. Problems involving high dimensional feature vectors usually require much computational cost and huge storage space. Moreover, it may additionally suffer from low accuracy because of the noise in data. In this paper we propose a novel distance-adaptive dimensionality reduction framework, namely generalized Multidimensional Scaling, with linear coding time to create compact and discriminative BOF or SPM representations. Comparing with traditional MDS, our approach exhibits two advantages, on one hand it is adaptive to many measures; on the other hand, it is able to map arbitrary query points into the new space. Exhaustive experimental results show that a very low dimension of BOF or SPM is sufficient for the retrieval task without losing accuracy. Comparatively, the state-of-the-art methods cannot achieve high accuracy on the low dimension. Aside from image retrieval task, we also show that our approach is much more effective than the original histogram representations when applied in image classification task. [ABSTRACT FROM AUTHOR]

Bag-of-Features (BOF) and Spatial Pyramid Matching (SPM) representation have become well-established in image classification and retrieval, owing to its simplicity and good performance. However, the hyper dimensionality of BOF vectors always faces the difficulty of “curse of dimensionality”, and leads to huge computation and storage complexity. To create a compact and discriminative BOF representations, in this paper we propose a novel unsupervised dimensionality reduction framework for the histogram vectors. First, we build the dissimilarity matrix between every histogram pairs, and then perform Multidimensional Scaling technique to obtain a low-dimensional Euclidean embedding of the original BOF while simultaneously preserving the inherent neighborhood structure. The widely used metrics for measuring dissimilarity, including distance and kernel, are investigated to build the dissimilarity matrix as the input of our dimensionality reduction model. Extensive experiment results show that a very low dimension is sufficient for the learning tasks using BOF or SPM without losing the classification accuracy. Comparatively, the state-of-the-art methods cannot achieve high accuracy on the very low dimension. Furthermore, a compact representation of BOFs can improve the classification accuracy compared with the original BOF. Finally, we also show that our compact representation is effective for image retrieval tasks. [ABSTRACT FROM AUTHOR]

Abstract: Nearest neighbor queries in high-dimensional space are important in various applications. One-dimensional mapping is an efficient indexing method to speed up the k-nearest neighbor search, which can transform a high-dimensional point into a single-dimensional value indexed by a B+-tree. In this paper, we present a new one-dimensional indexing scheme based on extended B+-tree for k-nearest neighbor search in high-dimensional space. We first partition the high-dimensional dataset and perform Principal Component Analysis on each partition. The distance of each point to the center of the partition is indexed using a B+-tree, and the projection on the first principal component of each point is embedded into leaf node of the B+-tree. In the query, a new filter strategy according to the spatial relationship between the query point and the axis determined by the first principal component is applied to improve the query performance. We also present a novel k-nearest neighbor search algorithm which can guarantee the accuracy of query results. Extensive experiments have been indicative of the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Abstract: The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method for image retrieval in large database. Some extensions of VA-file have been proposed towards better query performance. However, all of these methods applying sequential scan need read the whole vector approximation file. In this paper, we present a new indexing structure based on vector approximation method, in which only a small part of approximation file need be accessed. First, principal component analysis is used to map multidimensional points to a 1D line. Then a B +-tree is built to index the approximate vector according to principal component. When performing k-nearest neighbor search, the partial distortion searching algorithm is used to reject the improper approximate vectors. Only a small set of approximate vectors need to be sequentially scanned during the search, which can reduce the CPU cost and I/O cost dramatically. Experiment results on large image databases show that the new approach provides a faster search speed than the other VA-file approaches. [Copyright &y& Elsevier]

Artificial bee colony algorithm (ABC) is an efficient and popular evolutionary algorithm (EAs), which has been attracted wide attention by researchers, and improved ABC with various characteristics (ABCs) have been proposed. It is widely acknowledged that the search operator is the core element in the performance of ABC. However, the generally designed search operators of ABCs are rotation-variable processes and are dependent mainly on the natural coordinates and, as a result, the performance of those ABC is limited. In this paper, the mathematical characteristic of the search operator is deeply analyzed, and on this basis, ABC with adaptive covariance matrix (ACoM-ABC) is proposed, the adaptive covariance matrix (ACoM) is used to establish a proper coordinates by making use of the population distribution information, which can relieve the dependence of ABC on the coordinates to a certain extent and improve the exploitation capability. To balance the exploitation and exploration abilities of ABC, the search operators of ABC are implemented on eigen coordinates and natural coordinates Then, to estimate the performance of ACoM-ABC, which compares with six ABCs and other six EAs, and tests on CEC2014. The excellent experimental result shows that ACoM-ABC is an efficient and outstanding algorithm. Moreover, the proposed algorithm is applied to hearing loss detection, and the experiment result shows that the overall accuracy is 96.67%, which higher than the other five state-of-the-art approaches about 1%. Therefore, the ACoM-ABC has the practicability for realistic problems. [ABSTRACT FROM AUTHOR]

Classic exponent-Fourier moments (EFMs) have been popularly used for image reconstruction and invariant classification. However, EFMs lack natively the translation and scaling-invariant; in addition, they exhibit two types of drawbacks, namely numerical instability and reconstruction error, which in turn influence their reconstruction capability and image classification accuracy. This study considers the challenge of defining modified EFMs (MEFMs), which are based on modified exponent polynomials. In our methods, the basis function of traditional EFMs is appropriately modified, and these modified basis functions are used to replace the original ones. The basis function of the proposed moments is composed of piecewise modified exponent polynomials modulated by a variable parameter exponential envelope. Various types of optimal-order moments can be established by slightly adjusting the bandwidth of the modified basis functions. Finally, we extend the rotation-invariant feature of previous works and propose a new method of scaling and rotation-invariant image recognition using the proposed moments in a log-polar coordinate domain. The translation invariance can then be achieved by an image projection operation, which is substituted for the traditional approach based on the calculation of image geometric moments. The experimental results demonstrate that the MEFMs perform better than traditional EFMs and other classic orthogonal moments including the latest image moments in terms of the image reconstruction capability and the invariant recognition accuracy of smoothing filters, in both noise-free and noisy conditions. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithms which are applied to various complex problems for their competitive performance and simple structures are a relatively popular paradigm of evolutionary algorithms (EAs). Although many variants based on the basic ABC algorithm have been developed, most variants have limited performance on non-separable problems. This is mainly because these variants usually focus on only individual quality information, but neglect population distribution information. To address this limitation, in this paper, we propose a new ABC with a cumulative covariance matrix mechanism (CCoM-ABC), in which the population distribution information in all previous generations is involved to guide the evolution process. Specifically, the CCoM-ABC algorithm applies a cumulative covariance matrix (CCoM) mechanism by which the population information of every generation is involved to improve the probability of finding the optimum. It also applies an adaptive selection mechanism by which the proper coordinate system used to generate candidate solutions can be dynamically selected between a natural one and an eigen one during the search process. Furthermore, for improving the search ability, a new search equation is adopted in the scout bees phase. A series of experiments are conducted on the CEC2014 to evaluate the performance of the proposed CCoM-ABC algorithm, and the results show that the CCoM-ABC outperforms state-of-the-art ABC variants and EAs in terms of convergence speed, solution accuracy, and numerical scalability. For validating the practicality of the proposed CCoM-ABC algorithm, we further apply it to optimize the parameters of the neural network for hearing loss (HL) detection. This study involves 60 brain images consisting of 20 left-sided HL, 20 right-side HL, and 20 healthy control images. The results show that this model outperforms state-of-the-art related methods in terms of overall accuracy by about 2%. [ABSTRACT FROM AUTHOR]

P 2 O 5 –Al 2 O 3 –SiO 2 -GeO 2 -Li 2 O glasses doped with 3 d-transition metal (TM) ions from Sc to Zn were prepared by the conventional melting-quenching method. Their optical properties were investigated. The oxidation states, which were revealed from the UV–vis–NIR transmittance spectra, excitation and emission spectra of the studied glasses, are Sc(III), Ti(III), V(III), Cr(III), Mn(II), Fe(II,III), Co(II), Ni(II), Cu(I, II) and Zn(II). Scandium exhibits weak, broad emission at 321 nm in aluminophosphate glass probably ascribed to the formation of the Sc Al center. Cr3+ emission at 890 nm due to the 4T 2 (4F)→4A 2 (4F) transition of Cr3+ ions at the weak crystal field octahedral sites was observed under excitation at 296, 457, 658, and 684 nm. Mn2+ emission at 603 nm with a distinct shoulder at ∼513 nm due to the 4T 1 (4G) → 6A 1 (6S) transition of Mn2+ ions in an octahedral coordination and tetrahedral coordination environment, respectively, were observed under excitation at 346 nm and 408 nm. Cu+ emission at 468 nm with excitation at around 290 nm was ascribed to transition from excited s state to ground d state (d9s→d10) of Cu+. No other 3dN TM ions are found to give strong visible emission in aluminophosphate glass at room temperature. Temperature dependent emission spectra for V, Cr, Mn, Fe, Cu doped aluminophosphate glass with laser excitation have also been investigated. The photograph of the prepared glass samples. Image 1 [ABSTRACT FROM AUTHOR]

As data collection increases, more and more sensitive data is being used to publish query results. This creates a significant risk of privacy disclosure. As a mathematically provable privacy theory, differential privacy (DP) provides a tool to resist background knowledge attacks. Fuzzy differential privacy (FDP) generalizes differential privacy by employing smaller sensitivity and supporting multiple similarity measures. Thus the output error can be reduced under FDP. Existing FDP mechanisms employ sliding window strategy, which perturb the true query value to an interval with this value as the midpoint to maintain similarity of outputs from neighboring datasets. It is still possible for an attacker to infer some sensitive information based on the difference between the left and right endpoints of the output range. To address this issue, this article present two solutions: fixed interval perturbation and infinite interval perturbation. These strategies perturb the true query values of two neighboring datasets to the same interval and provide fuzzy differential privacy protection for the dataset. We apply the proposed method to the privacy-preserving problem of bipartite graph subgraph counting and verify the effectiveness by experiments. [ABSTRACT FROM AUTHOR]

Approximate Nearest Neighbor search (ANN) is one of the most frequently used and yet expensive operations in the high-dimensional database, especially the multimedia database involving massive high-dimensional feature vectors. Recently, Locality-Sensitive Hashing and its variants have been generally acknowledged as the promising solutions to ANN search due to its excellent performance and ease of implementation. However, the existing LSH methods for external memory usually create a linear order relationship on the set of compound hash keys to rearrange the data set in the external storage, which incurs many false negatives. In this paper, we propose a surprising simple method to solve the ANN problem with high accuracy results and requiring only a limited number of random I/O. For the distance-preserving properties of LSH functions, the idea of collision counting is used to guarantee the accuracy of the returned neighbours. We convert the dynamic collision counting problem into the nearest neighbor search (NN) in Hamming space. To support the NN search in the hamming space, we establish the multi-index hashing structure to rearrange the binary hash keys and their corresponding objectives. Accessing the candidates consumes a limited number of random I/O and shorter response time. Experimental results show that our method can achieve higher accuracy of ANN results compared with the state-of-the-art methods including LSB, C2LSH and SK-LSH. [ABSTRACT FROM AUTHOR]

The location selection (ls) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalized ls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio (Pinocchio-vo) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques. [ABSTRACT FROM PUBLISHER]

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Patient prognosis is poor, and the existing therapeutic strategies for LUAD are far from satisfactory. Recently, targeting N6-methyladenosine (m6A) modification of RNA has been suggested as a potential strategy to impede tumor progression. However, the roles of m6A modification in LUAD tumorigenesis is unknown. Methods: Global m6A levels and expressions of m6A writers, erasers and readers were evaluated by RNA methylation assay, dot blot, immunoblotting, immunohistochemistry and ELISA in human LUAD, mouse models and cell lines. Cell viability, 3D-spheroid generation, in vivo LUAD formation, experiments in cell- and patient-derived xenograft mice and survival analysis were conducted to explore the impact of m6A on LUAD. The RNA-protein interactions, translation, putative m6A sites and glycolysis were explored in the investigation of the mechanism underlying how m6A stimulates tumorigenesis. Results: The elevation of global m6A level in most human LUAD specimens resulted from the combined upregulation of m6A writer methyltransferase 3 (METTL3) and downregulation of eraser alkB homolog 5 (ALKBH5). Elevated global m6A level was associated with a poor overall survival in LUAD patients. Reducing m6A levels by knocking out METTL3 and overexpressing ALKBH5 suppressed 3D-spheroid generation in LUAD cells and intra-pulmonary tumor formation in mice. Mechanistically, m6A-dependent stimulation of glycolysis and tumorigenesis occurred via enolase 1 (ENO1). ENO1 mRNA was m6A methylated at 359 A, which facilitated it's binding with the m6A reader YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) and resulted in enhanced translation of ENO1. ENO1 positively correlated with METTL3 and global m6A levels, and negatively correlated with ALKBH5 in human LUAD. In addition, m6A-dependent elevation of ENO1 was associated with LUAD progression. In preclinical models, tumors with a higher global m6A level showed a more sensitive response to the inhibition of pan-methylation, glycolysis and ENO activity in LUAD. Conclusions: The m6A-dependent stimulation of glycolysis and tumorigenesis in LUAD is at least partially orchestrated by the upregulation of METTL3, downregulation of ALKBH5, and stimulation of YTHDF1-mediated ENO1 translation. Blocking this mechanism may represent a potential treatment strategy for m6A-dependent LUAD. [ABSTRACT FROM AUTHOR]

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity. Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications. [ABSTRACT FROM AUTHOR]

Background: Ferroptosis, a form of regulated cell death, is an important topic in the field of cancer research. However, the signalling pathways and factors that sensitise tumour cells to ferroptosis remain elusive. Methods: We determined the level of ferroptosis in cells by measuring cell death and lipid reactive oxygen species (ROS) production. The expression of RB1‐inducible coiled‐coil 1 (RB1CC1) and related proteins was analyzed by immunoblotting and immunohistochemistry. Immunofluorescence was used to determine the subcellular localization of RB1CC1. We investigated the mechanism of RB1CC1 nuclear translocation by constructing a series of RB1CC1 variants. To examine the ferroptosis‐ and RB1CC1‐dependent transcriptional program in tumour cells, chromatin immunoprecipitation sequencing was performed. To assess the effect of c‐Jun N‐terminal kinase (JNK) agonists on strenthening imidazole ketone erastin (IKE) therapy, we constructed cell‐derived xenograft mouse models. Mouse models of hepatocellular carcinoma to elucidate the importance of Rb1cc1 in IKE‐based therapy of liver tumourigenesis. Results: RB1CC1 is upregulated by lipid ROS and that nuclear translocation of phosphorylation of RB1CC1 at Ser537 was essential for sensitising ferroptosis in tumour cells. Upon ferroptosis induction, nuclear RB1CC1 sharing forkhead box (FOX)‐binding motifs recruits elongator acetyltransferase complex subunit 3 (ELP3) to strengthen H4K12Ac histone modifications within enhancers linked to ferroptosis. This also stimulated transcription of ferroptosis‐associated genes, such as coiled‐coil–helix–coiled‐coil–helix domain containing 3 (CHCHD3), which enhanced mitochondrial function to elevate mitochondrial ROS early following induction of ferroptosis. FDA‐approved JNK activators reinforced RB1CC1 nuclear translocation and sensitised cells to ferroptosis, which strongly suggested that JNK is upstream of RB1CC1. Nuclear localisation of RB1CC1 correlated with lipid peroxidation in clinical lung cancer specimens. Rb1cc1 was essential for ferroptosis agonists to suppress liver tumourigenesis in mice. Conclusions: Our findings indicate that RB1CC1‐associated signalling sensitises tumour cells to ferroptosis and that targeting RB1CC1 may be beneficial for tumour treatment. [ABSTRACT FROM AUTHOR]

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Patient prognosis is poor, and the existing therapeutic strategies for LUAD are far from satisfactory. Recently, targeting N6-methyladenosine (m6A) modification of RNA has been suggested as a potential strategy to impede tumor progression. However, the roles of m6A modification in LUAD tumorigenesis is unknown. Methods: Global m6A levels and expressions of m6A writers, erasers and readers were evaluated by RNA methylation assay, dot blot, immunoblotting, immunohistochemistry and ELISA in human LUAD, mouse models and cell lines. Cell viability, 3D-spheroid generation, in vivo LUAD formation, experiments in cell- and patient-derived xenograft mice and survival analysis were conducted to explore the impact of m6A on LUAD. The RNA-protein interactions, translation, putative m6A sites and glycolysis were explored in the investigation of the mechanism underlying how m6A stimulates tumorigenesis. Results: The elevation of global m6A level in most human LUAD specimens resulted from the combined upregulation of m6A writer methyltransferase 3 (METTL3) and downregulation of eraser alkB homolog 5 (ALKBH5). Elevated global m6A level was associated with a poor overall survival in LUAD patients. Reducing m6A levels by knocking out METTL3 and overexpressing ALKBH5 suppressed 3D-spheroid generation in LUAD cells and intra-pulmonary tumor formation in mice. Mechanistically, m6A-dependent stimulation of glycolysis and tumorigenesis occurred via enolase 1 (ENO1). ENO1 mRNA was m6A methylated at 359 A, which facilitated it's binding with the m6A reader YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) and resulted in enhanced translation of ENO1. ENO1 positively correlated with METTL3 and global m6A levels, and negatively correlated with ALKBH5 in human LUAD. In addition, m6A-dependent elevation of ENO1 was associated with LUAD progression. In preclinical models, tumors with a higher global m6A level showed a more sensitive response to the inhibition of pan-methylation, glycolysis and ENO activity in LUAD. Conclusions: The m6A-dependent stimulation of glycolysis and tumorigenesis in LUAD is at least partially orchestrated by the upregulation of METTL3, downregulation of ALKBH5, and stimulation of YTHDF1-mediated ENO1 translation. Blocking this mechanism may represent a potential treatment strategy for m6A-dependent LUAD. [ABSTRACT FROM AUTHOR]

Abstract: Color histogram is an important technique for color image database indexing and retrieving. However, the main problem with color histogram indexing is that it does not take the color spatial distribution into consideration. Previous researches have proved that the effectiveness of image retrieval increases when spatial feature of colors is included in image retrieval. In this paper, two new descriptors, color distribution entropy (CDE) and improved CDE (I-CDE), which introduce entropy to describe the spatial information of colors, are presented. In comparison with the spatial chromatic histogram (SCH) and geostat which also measure the global spatial relationship of colors, the experiment results show that CDE and I-CDE give better performance than SCH and geostat. [Copyright &y& Elsevier]

The m6A reader YT521-B homology containing 2 (YTHDC2) has been identified to inhibit lung adenocarcinoma (LUAD) tumorigenesis by suppressing solute carrier 7A11 (SLC7A11)-dependent antioxidant function. SLC7A11 is a major functional subunit of system X C −. Inhibition of system X C − can induce ferroptosis. However, whether suppressing SLC7A11 is sufficient for YTHDC2 to be an endogenous ferroptosis inducer in LUAD is unknown. Here, we found that induction of YTHDC2 to a high level can induce ferroptosis in LUAD cells but not in lung and bronchus epithelial cells. In addition to SLC7A11, solute carrier 3A2 (SLC3A2), another subunit of system X C − was equally important for YTHDC2-induced ferroptosis. YTHDC2 m6A-dependently destabilized Homeo box A13 (HOXA13) mRNA because a potential m6A recognition site was identified within its 3′ untranslated region (3′UTR). Interestingly, HOXA13 acted as a transcription factor to stimulate SLC3A2 expression. Thereby, YTHDC2 suppressed SLC3A2 via inhibiting HOXA13 in an m6A-indirect manner. Mouse experiments further confirmed the associations among YTHDC2, SLC3A2 and HOXA13, and demonstrated that SLC3A2 and SLC7A11 were both important for YTHDC2-impaired tumor growth and –induced lipid peroxidation in vivo. Moreover, higher expression of SLC7A11, SLC3A2 and HOXA13 indicate poorer clinical outcome in YTHDC2-suppressed LUAD patients. In conclusion, YTHDC2 is believed to be a powerful endogenous ferroptosis inducer and targeting SLC3A2 subunit of system X C − is essential for this process. Increasing YTHDC2 is an alternative ferroptosis-based therapy to treat LUAD. [Display omitted] • YTHDC2 induces ferroptosis via targeting both two subunits of system X C −. • YTHDC2 suppresses SLC3A2 subunit via inhibiting HOXA13-mediated SLC3A2 transcription. • HOXA1 3 mRNA is destabilized by YTHDC2 via an m6A-dependent manner. • Induction of YTHDC2 impairs tumor growth and induced lipid peroxidation in vivo. • YTHDC2 anti-correlates with SLC7A11, SLC3A2 and HOXA13 in LUAD patients. [ABSTRACT FROM AUTHOR]

Spatial temporal imputation is an important part of intelligent transportation system, and spatial temporal data missing also largely affects the performance of its downstream tasks. Spatial temporal data are affected by spatial, temporal, and unexpected conditions, and has a complex change pattern. Existing researches mostly focus on time series and often ignore the modeling of spatial dependency. Moreover, the imputation performance is poor under high missing rate or block missing scenarios, needing to enhance the model robustness. Therefore, we proposed a spatio-temporal generative adversarial imputation network (named STGAIN) to handle with the complex spatial temporal dependency and the diversity of missing conditions on spatio-temporal data. It includes a spatio-temporal generator and a spatio-temporal discriminator, which uses a graph convolutional neural network as a spatial aggregator and a one-dimensional deformable convolutional neural network as a temporal extractor. The generator observes the unmissing part data and the spatial relationships to impute the missing parts. The discriminator then takes the completed data as input and attempts to determine which components were actually observed and which were imputed. The spatio-temporal modeling ability was improved in the competition with each other. In order to make the generator in GAN distinguish the observed part from the missing part, a fusion loss function (including reconstruction loss and BCE loss) was designed for the generator training. Finally, experiments were conducted on two real highway datasets for different missing rates. And we test the model robustness under different size of dataset and noise interference. The results show that STGAIN significantly outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Spatio-temporal data are critical for intelligent systems, such as smart transportation and smart cities. However, due to sensor failure or power failure, the spatiotemporal data missing tends to have a big impact on downstream tasks. Meanwhile, if sensors are scarce, some spatial positions without sensors need data enhancement through intelligent methods. Existing workarounds focus on modeling temporal information (such as time series), often ignoring spatial dependency, or modeling the spatial and temporal domain separately for imputation. In this paper, we propose a Long-term Multi-dimensional Spatial–Temporal Graph Convolution Network (LMSTGCN), which not only inductively estimates some missing data, but also achieves data augmentation of target locations. It contains a periodic temporal encoding mechanism, a gated temporal capture module, and a multi-dimensional spatial–temporal GCN module. The long-term temporal dependencies are captured by the periodic temporal encoding mechanism. The spatial and extra-short-term temporal dependencies are simultaneously modeled by the multi-dimensional GCN module, which can achieve exponential growth in the range of receptive fields. Corresponding to this module, we designed a spatiotemporal adjacency matrix construction method. It generates spatiotemporal adjacency matrices of corresponding time length as needed. The short-term dependencies in sequences are captured by the gated temporal capture module. In experimental analysis, results demonstrate that the proposed model outperforms the state-of-the-art baselines on real-world data sets. [ABSTRACT FROM AUTHOR]

Long term dependency capture is essentially important for time series prediction and spatial–temporal forecasting. In recent years, many deep learning-based forecasting methods have been proposed, leading to rapid development in this area. We systematically reviewed long-term dependency capture methods, including temporal dependency in sequence (named intra-sequence temporal dependency), temporal dependency out of sequence (named inter-sequence temporal dependency, in this scenario the long-term dependencies are split by many subsequences). Because the batch technique is widely adopted in machine learning and deep learning, the range of temporal capturing ability for many proposed methods is intra-sequence temporal dependency, which limits the capacity of long-term dependency capture. Aiming at the above problems, we designed three type memory mechanisms (i.e., a temporal encoding memory mechanism, a cross-sequence memory mechanism and a query-key based memory mechanism) to solve those long term dependency problems. Moreover, based on the cross-sequence memory mechanism and query-key architecture, an Attention-based Long-Term Dependency Capture model (ALTDC) is proposed for long-term dependency modeling and further solves the temporal dependency coherence problem. ALTDC includes temporal Transformer and spatial Transformer. The temporal Transformer adopts multi-head attention mechanism in temporal dimension and takes into consideration the relative position encoding. The spatial Transformer leverages attention mechanism in spatial dimension, and utilizes learnable position encoding and graph convolution to capture spatial relationships. Experiments demonstrate that the proposed model outperforms the state-of-art baselines on real world time-series datasets and spatial–temporal datasets. [ABSTRACT FROM AUTHOR]

Background: Ferroptosis is the process of cell death triggered by lipid peroxides, and inhibition of glutathione (GSH) synthesis leads to ferroptosis. Liver cancer progression is closely linked to ferroptosis suppression. However, the mechanism by which inhibition of GSH synthesis suppresses potential ferroptosis of liver cancer cells and whether ferroptosis-related liver cancer biomarkers have a promising diagnostic value remain unknown. Methods: Ribonucleotide reductase regulatory subunit M2 (RRM2) levels were measured using an enzyme linked immunosorbent assay (ELISA), quantitative RT-PCR (qPCR), immunoblotting (IB) and immunochemistry (IHC). Cell viability and cell death were measured by a CellTiter-Glo luminescent cell viability assay and staining with SYTOX Green followed by flow cytometry, respectively. Metabolites were measured using the indicated kits. The Interaction between glutathione synthetase (GSS) and RRM2 was measured using immunofluorescence (IF), co-immunoprecipitation (co-IP) and the proximal ligation assay (PLA). The diagnostic value was analyzed using the area under the receiver operating characteristic curve (AUC-ROC). Bioinformatics analysis was performed using the indicated database. Results: RRM2 showed specifically elevated levels in liver cancer and inhibited ferroptosis by stimulating GSH synthesis via GSS. Mechanistically, phosphorylation of RRM2 at the Threonine 33 residue (T33) was maintained at normal levels to block the RRM2–GSS interaction and therefore protected RRM2 and GSS from further proteasome degradation. However, under ferroptotic stress, RRM2 was dephosphorylated at T33, thus the RRM2–GSS interaction was promoted. This resulted in the translocation of RRM2 and GSS to the proteasome for simultaneous degradation. Clinically, serum RRM2 was significantly associated with serum alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma glutamyl transpeptidase (γ-GT), albumin (ALB) and total bilirubin. The AUC-ROC for the combination of RRM2 with AFP was 0.947, with a sensitivity of 88.7% and a specificity of 97.0%, which indicates better diagnostic performance compared to either RRM2 or AFP alone. Conclusion: RRM2 exerts an anti-ferroptotic role in liver cancer cells by sustaining GSH synthesis. Serum RRM2 will be useful as a biomarker to evaluate the degree to which ferroptosis is suppressed and improve diagnostic efficiency for liver cancer. [ABSTRACT FROM AUTHOR]

Protein modifications have significant effects on tumorigenesis. N-Myristoylation is one of the most important lipidation modifications, and N-myristoyltransferase 1 (NMT1) is the main enzyme required for this process. However, the mechanism underlying how NMT1 modulates tumorigenesis remains largely unclear. Here, we found that NMT1 sustains cell adhesion and suppresses tumor cell migration. Intracellular adhesion molecule 1 (ICAM-1) was a potential functional downstream effector of NMT1, and its N-terminus could be N-myristoylated. NMT1 prevented ubiquitination and proteasome degradation of ICAM-1 by inhibiting Ub E3 ligase F-box protein 4, which prolonged the half-life of ICAM-1 protein. Correlations between NMT1 and ICAM-1 were observed in liver and lung cancers, which were associated with metastasis and overall survival. Therefore, carefully designed strategies focusing on NMT1 and its downstream effectors might be helpful to treat tumors. • NMT1 modulates cell adhesion and migration via ICAM-1 in tumor cells, • The N-terminus of ICAM-1 is essential for N-myristoylation and functions. • NMT1 protects ICAM-1 from being ubiquitinated by FBXO4. • NMT1 sustains ICAM-1 protein stability and functions. • Close correlations among NMT1 and ICAM-1 are revealed in tumors. [ABSTRACT FROM AUTHOR]

A multilevel fuzzy comprehensive evaluation approach based on multi-objective decision theory is used to assess tool cutting performance during the machining of titanium alloy. This includes an exponential scale of AHP weights for each index. We studied two different kinds of cutting tools, one with an applied micro-texture and conducted a comprehensive evaluation of their performance when engaged in the ordinary milling of titanium alloy. This was based on surface roughness, surface hardening, chip formation, and tool wear. We also undertook a comparison of the required cutting force and cutting temperature. The results show that in the process of cutting titanium alloy, the micro-texture of the surface has an anti-wear effect and stores impurities. This can improve a cutting tool’s workpiece and chip contact environment as well as its actual processing performance. The overall evaluation value for a micro-textured ball end milling cutter was found to be 0.62. Against this, the value for a common milling cutter was 0.38. These results form an important basis for improving the machinability of titanium alloy. [ABSTRACT FROM AUTHOR]

Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs and LSTM (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model’s performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths. The quantitative results demonstrated that our proposed model offers exceptional performance with mean accuracy (92.22), sensitivity (88.55), specificity (94.31) and AUC (97.18). Moreover, the model achieves real-time prediction with only 27.6ms for single sequence, and simultaneously predicts sequence data with lengths of 3–5. Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields. [ABSTRACT FROM AUTHOR]

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model. [ABSTRACT FROM AUTHOR]

Abstract: We reported the growth and its electrical properties of [100]-oriented diamond film using alcohol and hydrogen by HFCVD. SEM and Raman measurements indicated that high quality polycrystalline diamond films with [100]-faced structure were obtained. Dark current–voltage (I–V), capacitance–frequency (C–F), capacitance–voltage (C–V) and photocurrent under steady-state 55Fe 5.9keV X-ray excitation of freestanding diamond film were investigated at room temperature. Results indicated that after post-annealing for [100]-oriented diamond films dark current was in the order of 10−10 A and the photocurrent was of in the order 10−8 A by X-ray irradiation with the applied voltage of 40V, capacitance and dielectric loss were very small with the value of 2pF and 2×10−3 with the bias voltage of 0.05V, respectively, and almost had no variation with the change of frequency in high frequencies from 100kHz to 10MHz. [Copyright &y& Elsevier]